{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T14:02:31Z","timestamp":1791986551065,"version":"4.4.0"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Covariate adjustment can improve precision in analysing randomized experiments. With fully observed data, regression adjustment and propensity score weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with the inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks down. Regression adjustment no longer ensures efficiency gain over unadjusted analysis unless the true outcome model is linear in covariates or the outcomes are missing completely at random. Propensity score weighting, in contrast, still guarantees efficiency over unadjusted analysis, and including more covariates in adjustment never harms asymptotic efficiency. Moreover, we establish the value of using partially observed covariates to secure additional efficiency by the missingness indicator method, which imputes all missing covariates by zero and uses the union of the completed covariates and corresponding missingness indicators as the new, fully observed covariates. Based on these findings, we recommend using regression adjustment in combination with the missingness indicator method if the linear outcome model or missing-completely-at-random assumption is plausible and using propensity score weighting with the missingness indicator method otherwise.<\/jats:p>","DOI":"10.1093\/biomet\/asae017","type":"journal-article","created":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T01:03:40Z","timestamp":1711155820000},"page":"1413-1420","source":"Crossref","is-referenced-by-count":13,"title":["Covariate adjustment in randomized experiments with missing outcomes and covariates"],"prefix":"10.1093","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2024-1680","authenticated-orcid":false,"given":"Anqi","family":"Zhao","sequence":"first","affiliation":[{"name":"Fuqua School of Business, Duke University , 100 Fuqua Drive , Durham, North Carolina 27708,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2704-2353","authenticated-orcid":false,"given":"Peng","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California , Berkeley, 425 Evans Hall , Berkeley, California 94720,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Duke University , 214 Old Chemistry, Box 90251 , Durham, North Carolina 27708,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"2024112207520250500_asae017-B1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.chest.2016.03.019","article-title":"Motivational enhancement for increasing adherence to CPAP: a randomized controlled trial","volume":"150","author":"Bakker","year":"2016","journal-title":"Chest"},{"key":"2024112207520250500_asae017-B2","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1002\/sim.9840","article-title":"Covariate adjustment in randomized clinical trials with missing covariate and outcome data","volume":"42","author":"Chang","year":"2023","journal-title":"Statist. 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